Text mining method and apparatus for extracting features of documents

ABSTRACT

Concerning feature extraction of documents in text mining, a method and an apparatus for extracting features having the same nature as those by LSA are provided that require smaller memory space and simpler program and apparatus than the apparatus for executing LSA. Features of each document are extracted by feature extracting acts on the basis of a term-document matrix updated by term-document updating acts and of a basis vector, spanning a space of effective features, calculated by basis vector calculations. Execution of respective acts is repeated until a predetermined requirement given by a user is satisfied.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Japanese Patent Application No.2000-197421 filed on Jun. 29, 2000, the contents of which isincorporated herein by specific reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a text mining method and apparatus forextracting features of documents. In particular, the invention relatesto a text mining method and apparatus for extracting features ofdocuments, wherein features are extracted such that all mutuallyassociated documents and terms are placed near each other in the featurespace. Applications of the invention include document and/or webretrieval, associated term retrieval, document classification.

2. Description of the Related Art

In text mining as a technology for squeezing desired knowledge orinformation by making analysis of text data, effective featureextraction of documents is an important task for efficiently performingdocument and/or web retrieval, associated term retrieval, documentclassification and so on. As a typical document feature extractingmethod, a vector-space model as set out on page 313 of “Automatic TextProcessing” (Addison-Wesley, 1989) is frequently used.

In the vector-space model, when terms selected as indices in thedocuments, namely index terms representing the contents of thedocuments, are t in number, a vector V_(i) is used respectively tocorrespond to an index term T_(i) to define a t-dimensional vectorspace. All vectors forming thus the defined vector space can beexpressed as a linear combination of t in number of the vectorscorresponding to t in number of the index terms. In this vector space, adocument D_(r) is expressed as follows: $\begin{matrix}{D_{r} = {\sum\limits_{i = 1}^{i}{x_{ir}V_{i}}}} & (1)\end{matrix}$

In the foregoing expression (1), x_(ir) active on V_(i) is thecontribution of the index term T_(i) to the document D_(r) andrepresents a feature of the document. The feature is an amountrepresenting the term frequency of the index term in the document. Avector [x_(r1), x_(r2), . . . x_(rt)]′ of t×1 (t rows and one column)becomes a feature vector of the document D_(r). As the simplest case,when the index term T_(i) appears in the document D_(r), x_(ir) is setto 1. When the index term T_(i) does not appear in the document D_(r),x_(ir) is set to 0. In a more complicated case, as set forth in theforegoing publication on page 279 to 280, two quantities are used. Thesetwo quantities are a term frequency tf_(ri) of the index term T_(i) inthe document D_(r) and a document frequency df_(i) of documentscontaining the index term T_(i) in all documents registered in thedocument database.

For the group of documents consisting of d in number of documents, a t×dterm-document matrix X can be defined as follows:X=[x ₁ , x ₂ , . . . , x _(d)]

Here, a t-dimensional vector x_(j)=[x_(j1), x_(j2), . . . , x_(jt)]′expresses the feature vector of the document D_(j), and ′(dash)represents matrix inversion.

FIG. 1 is an illustration showing one example of documents, translatedfrom Japanese sentences, registered in a document database, where“ronin” is a romanized word meaning students who, having failed a schoolentrance-exam of a particular academic year, are preparing for one nextyear. FIG. 2 is an illustration showing one example of a term-documentmatrix taking the Kanji (Chinese) characters appearing on the documentsshown in FIG. 1 as index terms. Kanji terms are underlined in FIG. 1. InFIG. 2, among a character string “let me know about” appearing in all ofthe documents 1 to 3, the Kanji term “know” is checked off from theindex terms. FIG. 3 is an illustration showing one example of an actualinput question, translated from Japanese, from a user, where Kanji termsare underlined. If the index terms of FIG. 2 are used to express thequestion, the question can be expressed with the term-document matrixshown in FIG. 4.

In general, when the vector-space model is used, similarity sim (D_(r),D_(s)) of two documents D_(r) and D_(s) can be expressed as follows:$\begin{matrix}{{{sim}\left( {D_{r},D_{s}} \right)} = \frac{\sum\limits_{i = 1}^{t}{x_{ir}x_{is}}}{\sqrt{\sum\limits_{i = 1}^{t}{x_{ir}^{2}{\sum\limits_{i = 1}^{t}x_{is}^{2}}}}}} & (2)\end{matrix}$

When the similarity of the question and each document of FIG. 1 isjudged on the basis of the meaning of the question of FIG. 3, thequestion of FIG. 3 is the most similar to the document 3 of FIG. 1.However, using the feature vectors as shown in FIGS. 2 and 4, thesimilarity of each document of FIG. 1 and the question of FIG. 3 isrespectively sim(document 1, question)=0.5477, sim(document 2,question)=0.5477, sim(document 3, question)=0.5477. In short, all havethe same similarity.

As a solution for such a problem, a method called Latent SemanticAnalysis (LSA) was proposed in “Journal of the American Society forInformation Science” 1990, Vol. 41, No. 6, pp. 391 to 407. This methodextracts latent meaning of the documents on the basis of co-occurrencesof the terms and is significantly outstanding in terms of retrievingefficiency. Here, “co-occurrences of terms” represents a situation wherethe terms appear simultaneously in the same documents/statements.

The LSA extracts a latent semantic structure of the documents byperforming singular value decomposition (SVD) for the term-documentmatrix. In the obtained feature space, mutually associated documents andterms are located near each other. In a report placed in “BehaviorResearch Methods Instruments & Computers” (1991), Vol. 23, No. 2, pp.229 to 236, retrieval using the LSA indicates a result of 30% higherefficiency in comparison with the vector-space model. LSA will beexplained hereinafter in more detail.

In LSA, at first, singular value decomposition is performed for the t×dterm-document matrix X as set out below.X=T₀ S ₀ D ₀′  (3)

Here, T₀ represents an orthogonal matrix of t×m, S₀ represents a squarediagonal matrix of m×m with taking m in number of the singular values asthe diagonal elements and setting 0 to the other elements. D₀′represents an orthogonal matrix of m×d. In addition, let us assume that0≧d≧t, and arrange the orthogonal elements of S₀ in descending order.

Furthermore, in LSA, with respect to the feature vector x_(q) of t×1 ofa document D_(q), the following conversion is performed to derive a LSAfeature vector y_(q) of n×1;y _(q)=S⁻¹T′x_(q)  (4)

Here, S is a square diagonal matrix of n×n taking the first to (n)th ofthe diagonal elements of S₀, and T is a matrix of t×n drawing the firstto (n)th columns of T₀.

As an example, results of singular value decomposition of theterm-document matrix shown in FIG. 2 are given below. The matrices T₀,S₀ and D₀ are expressed as follows: $T_{0} = \begin{bmatrix}0.1787 & {- 0.3162} & 0.3393 \\0.1787 & {- 0.3162} & 0.3393 \\0.1787 & {- 0.3162} & 0.3393 \\0.4314 & {- 0.3162} & {- 0.1405} \\0.4314 & {- 0.3162} & {- 0.1405} \\0.1787 & 0.3162 & 0.3393 \\0.1787 & 0.3162 & 0.3393 \\0.4314 & 0.3162 & {- 0.1405} \\0.4314 & 0.3162 & {- 0.1405} \\0.1787 & 0.3162 & 0.3393 \\0.2527 & 0.0000 & 0.4798\end{bmatrix}$ $S_{0} = \begin{bmatrix}2.7979 & 0 & 0 \\0 & 2.2361 & 0 \\0 & 0 & 1.4736\end{bmatrix}$ $D_{0} = \begin{bmatrix}0.5000 & {- 0.7071} & 0.5000 \\0.5000 & 0.7071 & 0.5000 \\0.7071 & 0.0000 & {- 0.7071}\end{bmatrix}$

Let us assume that the dimension t of the LSA feature vectors is 2 andapplying the foregoing expression (4) to each feature vector of theterm-document matrix in FIG. 2. Then, the LSA feature vectors of thedocuments 1, 2 and 3 are respectively [0.5000, −0.7071]′, [0.5000,0.7071]′ and [0.7071, 0.0000]′. In addition, applying the foregoingexpression (4) to the feature vector of FIG. 4, the LSA feature vectorof the question from the user becomes [0.6542, 0]′.

Applying the foregoing expression (2) to the LSA feature vectorsobtained as set forth above, the similarity of the question of FIG. 3and each document of FIG. 1, become respectively, sim(document 1,question)=0.5774, sim(document 2, question)=0.5774, and sim(document 3,question)=1.0000. Thus, a result that the document 3 has the highestsimilarity to the question can be obtained. Considering a help systemapplication or the like utilizing computer networks, an answer statementof the document 3 registered in the document database will be returnedto the user who asked the question of FIG. 3.

For singular value decomposition, an algorithm proposed in “MatrixComputations”, The Johns Hopkins University Press, 1996, pp. 455 to 457,is frequently used. In the report of “Journal of the American Societyfor Information Science” set forth above, there is a statement that thevalue of the number of rows (or columns) n of the square matrix S ispreferably about 50 to 150. In addition, in the foregoing report of“Behavior Research Methods, Instruments, & Computers”, it has beenindicated that better efficiency can be attained by pre-processing usingthe term frequency or document frequency instead of simply setting eachelement of the feature vector to 0 or 1 before performing LSA.

However, in the algorithm for singular value decomposition proposed inthe foregoing “Matrix Computations”, memory space in the order of thesquare of the number of index terms t (t²) is required at the minimum.This is because a matrix of t×t is utilized for bidiagonalization of amatrix in the process of calculation of basis vectors spanning a featurespace from a given term-document matrix. The prior art is therefore notapplicable to document database holding a very large number of terms anddata. Furthermore, the prior art requires complicated operations ofmatrices irrespective of the number of data.

SUMMARY OF THE INVENTION

The present invention has been worked out in view of the problems setforth above. In a first aspect of the present invention, a text miningmethod is provided for extracting features of documents using aterm-document matrix consisting of vectors corresponding to index termsrepresenting the contents of the documents. In the term-document matrix,contributions of the index terms to each document act on respectiveelements of the term-document matrix. The method comprises:

-   -   a basis vector calculating step of calculating a basis vector        spanning a feature space, in which mutually associated documents        and terms are located in proximity with each other, based on a        steepest descent method minimizing a cost;    -   a feature extracting step of calculating a parameter for        normalizing features using the term-document matrix and the        basis vector, and extracting the features on the basis of the        parameter; and    -   a term-document matrix updating step of updating the        term-document matrix to a difference between the term-document        matrix, to which the basis vector is not applied, and the        term-document matrix, to which the basis vector is applied.

In a second aspect of the present invention, a text mining method isprovided for extracting features of documents wherein the cost isdefined as a second-order cost of the difference between theterm-document matrix, to which the basis vector is not applied, and theterm-document matrix, to which the basis vector is applied.

In a third aspect of the present invention, a text mining method isprovided for extracting features of documents wherein the basis vectorcalculating step comprises:

-   -   an initializing step of initializing a value of the basis        vector;    -   a basis vector updating step of updating the value of the basis        vector;    -   a variation degree calculating step of calculating a variation        degree of the value of the basis vector;    -   a judging step of making a judgment whether a repetition process        is to be terminated or not using the variation of the basis        vector; and    -   a counting step of counting the number of times of the        repetition process.

In a fourth aspect of the present invention, a text mining method isprovided for extracting features of documents wherein the basis vectorupdating step updates the basis vector using a current value of thebasis vector, the term-document matrix and an updating ratio controllingthe updating degree of the basis vector.

In a fifth aspect of the present invention, a text mining method isprovided for extracting features of documents wherein when all basisvectors and normalizing parameters required in extracting the featureshave been already obtained, the calculation of the normalizingparameters in the basis vector calculating step and the execution of thefeature extracting step are omitted. In addition, the feature extractingstep extracts the features using the basis vectors and the normalizingparameters that have been already obtained.

In a sixth aspect of the present invention, a text mining apparatus isprovided for extracting features of documents using a term-documentmatrix consisting of vectors corresponding to index terms representingthe contents of the documents. In the term-document matrix,contributions of the index terms to each document act on respectiveelements of the term-document matrix. The apparatus comprises:

-   -   basis vector calculating means for calculating a basis vector        spanning a feature space, in which mutually associated documents        and terms are located in proximity with each other, based on a        steepest descent method minimizing a cost;    -   feature extracting means for calculating a parameter for        normalizing features using the term-document matrix and the        basis vector, and extracting the features on the basis of the        parameter; and    -   term-document matrix updating means for updating the        term-document matrix to a difference between the term-document        matrix, to which the basis vector is not applied, and the        term-document matrix, to which the basis vector is applied.

In a seventh aspect of the present invention, a text mining apparatus isprovided for extracting features wherein the cost is defined as asecond-order cost of the difference between the term-document matrix, towhich the basis vector is not applied, and the term-document matrix, towhich the basis vector is applied.

In an eighth aspect of the present invention, a text mining apparatus isprovided for extracting features of documents wherein the basis vectorcalculating means comprises:

-   -   initializing means for initializing a value of the basis vector;    -   basis vector updating means for updating the value of the basis        vector;    -   variation degree calculating means for calculating a variation        degree of the value of the basis vector;    -   judging means for making a judgment whether a repetition process        is to be terminated or not using the variation of the basis        vector; and    -   counting means for counting the number of times of the        repetition process.

In a ninth aspect of the present invention, a text mining apparatus isprovided for extracting features of documents wherein the basis vectorupdating means updates the basis vector using a current value of thebasis vector, the term-document matrix and an updating ratio controllingthe updating degree of the basis vector.

In a tenth aspect of the present invention, a text mining apparatus isprovided for extracting features of documents wherein when all basisvectors and normalizing parameters required in extracting the featureshave been already obtained, the calculation of the normalizingparameters in the basis vector calculating means and the execution ofthe feature extracting means are omitted. In addition, the featureextracting means extracts the features using the basis vectors and thenormalizing parameters that have been already obtained.

In an eleventh aspect of the present invention, a computer programproduct is provided for being executed in a text mining apparatus forextracting features of documents using a term-document matrix consistingof vectors corresponding to index terms representing the contents of thedocuments. In the term-document matrix, contributions of the index termsact on respective elements of the term-document matrix. The computerprogram product comprises:

-   -   a basis vector calculating step of calculating a basis vector        spanning a feature space, in which mutually associated documents        and terms are located in proximity with each other, based on a        steepest descent method minimizing a cost;    -   a feature extracting step of calculating a parameter for        normalizing features using the term-document matrix and the        basis vector, and extracting the features on the basis of the        parameter; and    -   a term-document matrix updating step of updating the        term-document matrix to a difference between the term-document        matrix, to which the basis vector is not applied, and the        term-document matrix, to which the basis vector is applied.

The feature extracting apparatus disclosed in this specification isconstructed by defining a cost as a second-order function of adifference between the term-document matrix, to which the basis vectoris not applied, and the term-document matrix, to which the basis vectoris applied. The apparatus merely requires the following means:

-   -   a. basis vector calculating means for calculating a basis vector        applying a steepest descent method to the cost;    -   b. feature extracting means for calculating a parameter for        normalizing features using the term-document matrix and the        basis vector, and extracting the features on the basis of the        parameter;    -   c. term-document matrix updating means for updating the        term-document matrix to the difference between the term-document        matrix, to which the basis vector is not applied, and the        term-document matrix, to which the basis vector is applied, in        order to prevent redundant extraction of features; and    -   d. feature extraction control means for controlling execution of        respective means.

The basis vector calculating means repeats calculation on the basis ofthe input term-document matrix to finally derive one basis vector. Therepetition process is terminated when the variation degree of the basisvector becomes less than or equal to a predetermined reference value.

The feature extracting means calculates a parameter for normalizing thefeatures on the basis of the input basis vector and the term-documentmatrix, and extracts a feature for each document.

The term-document matrix updating means updates the term-document matrixon the basis of the input basis vector.

The feature extraction control means repeats execution of each meansuntil the number of the features defined by the user is satisfied. Whenthe basis vectors and the normalizing parameters have been alreadycalculated, execution of the basis vector calculating means andcalculation of the normalizing parameters in the feature extractingmeans are omitted. Then, the feature extraction can be performed withthe construction incorporating the already obtained basis vectors andthe normalizing parameters.

According to the present invention, a text mining method for extractingfeatures of documents using a term-document matrix consisting of vectorscorresponding to index terms representing the contents of the documents,wherein contributions of the index terms act on respective elements ofthe term-document matrix, comprises the following steps:

-   -   i. a basis vector calculating step of calculating a basis vector        spanning a feature space, wherein mutually associated documents        and terms are located in proximity with each other based on a        steepest descent method minimizing a cost;    -   ii. a feature extracting step of calculating a parameter for        normalizing features using the term-document matrix and the        basis vector, and extracting the features on the basis of the        parameter;    -   iii. a term-document matrix updating step of updating the        term-document matrix to a difference between the term-document        matrix, to which the basis vector is not applied, and the        term-document matrix, to which the basis vector is applied; and    -   iv. a feature extraction control step of controlling execution        of respective steps.

Therefore, concerning feature extraction of documents in text mining,the features having the same nature as those obtained by LSA can beextracted with smaller memory space than the apparatus or methodexecuting LSA. In addition, specific software or hardware for extractingthe features can be easily implemented.

The above and other objects, characteristics and advantages of differentembodiments of the present invention will become more apparent from thefollowing descriptions of embodiments thereof taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration showing one example of documents registered ina document database;

FIG. 2 is an illustration showing one example of a term-document matrixwith taking Kanji terms appearing in the documents shown in FIG. 1 asindex terms;

FIG. 3 is an illustration showing one example of a question actuallyinput by a user;

FIG. 4 is an illustration showing a term-document matrix obtained fromthe question in FIG. 3;

FIG. 5 is an illustration showing one embodiment of a feature extractingapparatus according to the present invention;

FIG. 6 is an illustration showing one example of a hardware constructionfor implementing the present invention;

FIG. 7 is an illustration showing a structure of a term-document matrixdata file;

FIG. 8 is an illustration showing a structure of a basis vector datafile, wherein the calculated basis vectors are stored;

FIG. 9 is an illustration showing a structure of a feature data file;

FIG. 10 is an illustration showing a structure of a normalizingparameter data file;

FIG. 11 is a flowchart showing calculation of a basis vector in basisvector calculating means; and

FIG. 12 is an illustration showing one example of an automatic documentclassifying system employing one embodiment of the feature extractingapparatus according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

FIG. 5 is an illustration showing one embodiment of a feature extractingapparatus according to the present invention. As shown in FIG. 5,feature extraction control means 200 has term-document matrix updatingmeans 210, basis vector calculating means 220, feature extracting means230. The reference numeral 100 denotes a term-document matrix data file,300 denotes a basis vector data file, 400 denotes a feature data file,450 denotes a normalizing parameter data file. In the term-documentmatrix data file 100, a term-document matrix of collected document datais stored. The term-document matrix updating means 210 reads theterm-document matrix from the term-document matrix data file 100, andtransfers the read term-document matrix to the basis vector calculatingmeans 220 and the feature extracting means 230 without updating theterm-document matrix, in a first iteration process.

In a second and subsequent iteration processes, the terms-documentmatrix is updated on the basis of the basis vector transferred from thebasis vector calculating means 220. The result of updating istransferred to the basis vector calculating means 220 and the featureextracting means 230. The basis vector calculating means 220 calculatesone basis vector through a repetition process based on the term-documentmatrix transferred from the term-document matrix updating means 210.Then, the degree of variation of the basis vector in respectiverepetition is monitored for terminating the repetition process when thedegree of variation becomes less than or equal to a predeterminedreference value. The basis vector calculating means 220 stores thecalculated basis vector in the basis vector data file 300 and inconjunction therewith, transfers the calculated basis vector to theterm-document matrix updating means 210 and the feature extraction means230. The feature extracting means 230 extracts one feature of eachdocument on the basis of the term-document matrix transferred from theterm-document matrix updating means 210 and the basis vector transferredfrom the basis vector calculating means 220. The result is stored in thefeature data file 400, and also the parameter for normalizing thefeatures is stored in the normalizing parameter data file 450.

Execution of the term-document matrix updating means 210, the basisvector calculating means 220 and the feature extracting means 230 istaken as one iteration process. Number of times of iteration processeswill be indicated by suffix i, and number of features designated by theuser is indicated by suffix n. The feature extraction control means 200repeats the process until a condition, i=n is satisfied. On the otherhand, in a case where all of the required basis vectors and the requirednormalizing parameters have already been obtained, execution of thebasis vector calculation means 220 and calculation of the normalizingparameters in the feature extracting means 230 may be omitted.Therefore, in such a case, the feature extraction control means 200 maybe constructed with the term-document matrix updating means 210incorporating the known basis vectors and normalizing parameters, andwith the feature extracting means 230.

FIG. 6 is an illustration showing one example of a hardware constructionfor implementing the present invention. As shown in FIG. 6, the featureextracting apparatus includes the following components:

-   -   a central processing unit (CPU) 10 performing control for the        overall apparatus,    -   a memory 20 for storing the program and providing a temporary        data storage region required for executing the program,    -   a keyboard 30 for inputting data, and    -   a display 40 for generating a display screen.

The programs to be executed by the feature extraction control means 200,the term-document matrix data file 100, the basis vector data file 300,the feature data file 400, and the normalizing parameter data file 450are stored in the memory 20.

By taking this construction, the feature extraction is performed by CPU10 receiving the command from the user through the keyboard 30, a mousepointing a desired position on the display 40, or the like. It should benoted that, in the example shown in FIG. 5, the feature extractioncontrol means 200 has a stand-alone construction. However, the featureextraction control means 200 may be built-in other systems.

FIG. 7 is an illustration showing a structure of the term-documentmatrix data file. In FIG. 7, the reference numerals 101-1, 101-2, . . ., 101-d correspond to t-dimensional term-document data A consisting of din number of data. Here, X=[x₁, x₂, . . . x_(d)], x_(j)=[x_(j1), x_(j2),. . . x_(jt)]′ are defined to express the term-document data A with at×d matrix X.

FIG. 8 is an illustration showing a structure of the basis vector datafile storing the calculated basis vectors. In FIG. 8, the referencenumerals 301-1, 301-2, . . . 301-n correspond to t-dimensional basisvector data B consisting of n in number of data. The (i)th element 301-icorresponds to an output value of the basis vector calculating means 220in the (i)th iteration process in FIG. 5. In the following disclosure,this element is expressed by a t×1 column vector w_(i)=[w_(i1), w_(i2),. . . , w_(it)]′.

FIG. 9 is an illustration showing a structure of the feature data file.In FIG. 9, the reference numerals 401-1, 401-2, . . . , 401-n correspondto d-dimensional feature data C consisting of n in number of data. The(i)th element 401-i corresponds to an output value of the feature by thefeature extraction means 230 in the (i)th iteration process in FIG. 5.This element is expressed by an 1×d row vector y_(i)=[y_(i1), y_(i2), .. . , y_(id)].

FIG. 10 is an illustration showing a structure of the normalizingparameter data file. In FIG. 10, the reference numerals 451-1, 451-2, .. . , 451-n correspond to normalizing parameter data D consisting of nin number of data. The (i)th element 451-i corresponds to an outputvalue of the normalizing parameter by the feature extracting means 230in the (i)th iteration process in FIG. 5.

Using the foregoing definitions, an implementation of feature extractionin the shown embodiment will be explained. The term-document matrixupdating means 210 reads out X from the term-document matrix data file100 only when i=1, namely in the first iteration process, to store in at×d matrix E without performing any arithmetic operation. Accordingly,E=[e₁, e₂, . . . , e_(d)], e_(j)=[e_(j1), e_(j2), . . . ,e_(jt)]′=[X_(j1), X_(j2), . . . , X_(jt)]′. In order to preventredundant extraction of the features extracted in the precedingiteration processes, E is updated in the (i)th iteration using thecurrent value and the basis vector calculated in the immediatelypreceding iteration process. The result of updating is transferred tothe basis vector calculating means 220. A value of E in the (i)thiteration, E(i), will be expressed by the following expression (5):$\begin{matrix}{{E(i)} = \left\{ \begin{matrix}{X,} & {{{for}\quad i} = 1} \\{{{E\left( {i - 1} \right)} - {w_{i - 1}\left( {w_{i - 1}^{\prime}{E\left( {i - 1} \right)}} \right)}},} & {otherwise}\end{matrix} \right.} & (5)\end{matrix}$

Here, E(i)=[e_(i)(i), e₂(i), . . . , e_(d)(i)], each element e_(j)(i) ofE(i) is defined by e_(j)(i)=[e_(j1)(i), e_(j2)(i), . . . , e_(jt)(i)]′.Namely, when i≧2, the term-document matrix is updated to a differencederived by subtracting the term-document matrix, to which the basisvector is applied, from the term-document matrix, to which the basisvector is not applied.

FIG. 11 is a flowchart showing calculation of the basis vector in thebasis vector calculating means. In FIG. 11, a value of w_(i) in the(k)th repetition is expressed by w_(i)(k)=[w_(i1)(k), w_(i2)(k), . . . ,w_(it)(k)]′. At first, at step S500, the suffix k is initialized to 1.Subsequently, the process is advanced to step S510 to initializerespective element of w_(i)(1) with an arbitrary value between −C to C.Here, the value of C may be a positive small value, such as C=0.01. Atstep S520, in order to calculate the basis vector spanning a featurespace where mutually associated documents and terms are located inproximity with each other, a second-order cost expressed by thefollowing expression (6) is provided. $\begin{matrix}{\frac{1}{2d}{\sum\limits_{m = 1}^{d}{\sum\limits_{l = 1}^{t}\left( {{e_{l\quad m}(i)} - {w_{li}{\overset{\sim}{y}}_{im}}} \right)^{2}}}} & (6)\end{matrix}$

Here, “terms are placed in proximity” means that the positions of theterms are close with each other within a feature space, and “documentsare placed in proximity” means that the positions of terms included inrespective documents are close in the feature space. On the other hand,a cost means an object to be minimized. In t the shown embodiment, thecost is defined as a second-order function of the difference between theterm-document matrix, to which the basis vector is not applied, and theterm-document matrix, to which the basis vector is applied, as expressedby the expression (6). Here,{tilde over (y)}_(im)

-   -   is the (m)th element of a 1×d vector {tilde over (y)}_(i) which        is defined as follows:        {tilde over (y)} ₁ =[{tilde over (y)} _(i1) , {tilde over (y)}        _(i2) , . . . , {tilde over (y)} _(id) ]=w _(i) ′E(i)  (7)

For the cost, the steepest descent method is applied to update the valueof w_(i) as expressed by the following expression (8). $\begin{matrix}{{w_{i}\left( {k + 1} \right)} = {{w_{i}(k)} + {\frac{\mu_{i}(k)}{d}\left( {{E(i)} - {{w_{i}(k)}{z_{i}(k)}}} \right){z_{i}(k)}^{\prime}}}} & (8)\end{matrix}$

Here, μ_(i)(k) is an update ratio controlling the degree of updating inthe (k)th repetition, which is initialized by a positive small valuewhen k is 1, such as μ_(i)(1)=0.1. Every time of increment of k, thevalue is decreased gradually. In the alternative, it is also possible toset the value at a constant value irrespective of k. On the other hand,z_(i)(k) is defined as follows:z _(i)(k)=w _(i)(k)′E(i)  (9)

At step S530, δ _(i)(k) indicating the degree of variation of w_(i) isderived as follows: $\begin{matrix}{{\delta_{i}(k)} = \sqrt{\sum\limits_{j = 1}^{i}\left( {{w_{ji}\left( {k + 1} \right)} - {w_{ji}(k)}} \right)^{2}}} & (10)\end{matrix}$

At step S540, a judgment is made whether the process is to be terminatedor not on the basis of the value of δ_(i)(k). As a result of thejudgment, if termination is determined, the process is advanced to stepS560, and otherwise, the process is advanced to step S550. Here, in FIG.11, β _(i) is a positive small value, such as β_(i)=1×10⁻⁶.

At step S550, the value of the counter k is incremented by 1. Then, theprocess is returned to step S520. At step S560, w_(i) is stored as the(i)th data of the basis vector data file 300. At the same time, w_(i) istransferred to the term-document matrix updating means 210 and thefeature extracting means 230. In the feature extracting means 230, thefeature y_(i) and the normalizing parameter p_(i) are calculated in thefollowing manner.y _(i) ={tilde over (y)} _(i) /p _(i)  (11)

Here, p_(i) is defined as follows: $\begin{matrix}{p_{i} = \sqrt{\sum\limits_{j = 1}^{d}{\overset{\sim}{y}}_{ij}^{2}}} & (12)\end{matrix}$

The feature y_(i) and the normalizing parameter p_(i) are storedrespectively in the feature data file 400 and the normalizing parameterdata file 450 as the (i)th data.

FIG. 12 is an illustration showing one example of an automatic documentclassifying system employing the shown embodiment of the featureextracting apparatus. In FIG. 12, the reference numeral 601 denotesterm-document matrix calculating means, 602 denotes classifying means.The classifying means 602 may be implemented by a method disclosed in“Journal of Intelligent and Fuzzy Systems”, published on 1993, Vol. 1,No. 1, Pages 1 to 25.

The document data stored in the document database E is taken in theautomatic document classifying system 600. In the automatic documentclassifying system 600, a term-document matrix is derived in theterm-document matrix calculating means 601. The result of calculation ofthe term-document matrix is transferred to the feature extractioncontrol means 200. The feature extraction control means 200 extracts thefeatures from the received term-document matrix. The extracted result isoutput to the classifying means 602. In the classifying means 602, theresult of classification is output on the basis of the input features.

To evaluate the present invention, feature extraction of actual documentdata related to an entrance examination system was performed. It hasbeen confirmed that the present invention could extract the features ofthe same nature as those extracted using the conventional LSA.

Next, concerning the size of the memory space, in a typically practicalcase where the number of terms t is significantly greater than number ofdocuments d (t>>d), the conventional LSA requires in the order of t² ofthe memory size, the present invention merely requires the memory sizein the order of t·d for calculating respective basis vectors.Furthermore, in order to realize the prior art, a complicated matrixoperation apparatus is required. The system according to the invention,however, can be easily realized with an apparatus that performs simplearithmetic operations. Namely, according to the present invention, theLSA feature extraction can be performed using a smaller memory space anda simpler program. In addition, this simple program may be loaded in adigital signal processor (DSP). Therefore, a specific chip for featureextraction can be produced easily.

Hereinafter, the results of respective means executing the shownembodiment of the feature extracting apparatus for the documents of FIG.1 and the question of FIG. 3 will be shown.

A. Documents of FIG. 1

First, let X denote the term-document matrix of FIG. 2.

I. First Iteration in Feature Extraction Control Means 200 (i=1)

According to the foregoing expression (5), the term-document matrixupdating means 210 outputs E(1) expressed by the following expression tothe basis vector calculating means 220 and the feature extracting means230. ${E(1)} = \begin{bmatrix}1 & 0 & 0 \\1 & 0 & 0 \\1 & 0 & 0 \\1 & 0 & 1 \\1 & 0 & 1 \\0 & 1 & 0 \\0 & 1 & 0 \\0 & 1 & 1 \\0 & 1 & 1 \\0 & 1 & 0 \\0 & 0 & 1\end{bmatrix}$

In the basis vector calculating means 220, initialization is performedwith setting the basis vector w_(i)(1) at [0.0100, −0.0100, 0.0100,−0.0100, 0.0100, −0.0100, 0.0100, −0.0100, 0.0 100, −0.0100, 0.0100]′,μ₁ at a fixed value 0.1, β₁ at 1×10⁻⁶. The calculation shown in FIG. 11is performed for a hundred thirty-two times. Then, the basis vectorw₁=[0.1787, 0.1787, 0.1787, 0.4314, 0.4314, 0.1787, 0.1787, 0.4314,0.4314, 0.1787, 0.2527]′is stored in the basis vector data file 300, andoutput to the feature extracting means 230 and the term-document matrixupdating means 210.

First Repetition in Basis Vector Calculating Means 220 (k=1)

From the foregoing expression (8),w ₁(2)=[0.0103 −0.0097, 0.0103, 0.0093, 0.0107, −0.0103, 0.0097,−0.0100, 0.0100, −0.0103, 0.0103]′w ₁(2)−w ₁(1)=10⁻³×[0.3332, 0.3334, 0.3332, 0.6668, 0.6666, −0.3332,−0.3334, 0.0001, −0.0001, −0.3332, 0.3332]′ δ ₁(1)=0.0103

Second Repetition in Basis Vector Calculating Means 220 (k=2)

From the foregoing expression (8),w ₁(3)=[0.0107, −0.0093, 0.0107, −0.0085, 0.0115, −0.0107, 0.0093,−0.0100, 0.0100, −0.0107, 0.0107]′w ₁(3)−w ₁(2)=10⁻³×[0.4110, 0.4112, 0.4110, 0.8001, 0.7998, −0.3665,−0.3668, 0.0224, 0.0221, −0.3665, 0.3887]′δ ₁(2)=0.0015

—syncopated—

A Hundred Thirty-Second Repetition in Basis Vector Calculating Means 220(k=132)

From the foregoing expression (8),w ₁(133)=[0.1787, 0.1787, 0.1787, 0.4314, 0.4314, 0.1787, 0.1787,0.4314, 0.4314, 0.1787, 0.2527]′w ₁(133)−w ₁(132)=10⁻⁶×[−0.3020, −0.3020, −0.3020, −0.3020, −0.3020,0.3020, 0.3020, 0.3020, 0.3020, 0.0000]′δ ₁(132)=9.5500×10⁻⁷

In the feature extracting means 230, the operations shown in theexpressions (11) and (12) are performed for outputting:

 y₁=[0.5000, 0.5000, 0.7071]

-   -   and        p₁=2.7979

to the feature data file 400 and the normalizing parameter data file450.

II. Second Iteration in Feature Extraction Control Means 200 (i=2)

In the term-document matrix updating means 210, from the foregoingexpression (5), E(2) expressed as follows is output to the basis vectorcalculating means 220 and the feature extracting means 230:${E(2)} = \begin{bmatrix}0.7500 & {- 0.2500} & {- 0.3536} \\0.7500 & {- 0.2500} & {- 0.3536} \\0.7500 & {- 0.2500} & {- 0.3536} \\0.3964 & {- 0.6036} & 0.1464 \\0.3964 & {- 0.6036} & 0.1464 \\{- 0.2500} & 0.7500 & {- 0.3535} \\{- 0.2500} & 0.7500 & {- 0.3535} \\{- 0.6036} & 0.3965 & 0.1465 \\{- 0.6036} & 0.3965 & 0.1465 \\{- 0.2500} & 0.7500 & {- 0.3535} \\{- 0.3536} & {- 0.3535} & 0.5000\end{bmatrix}$

In the basis vector calculating means 220, initialization is performedwith setting the basis vector w₂(1) at [0.0100, −0.0100, 0.0100,−0.0100, 0.0100, −0.0100, 0.0100, −0.0100, 0.0100, −0.0100, 0.0100]′, μ₂at a fixed value 0.1, β₂ at 1×10⁻⁶. The calculation shown in FIG. 11 isperformed for a hundred nineteen times. Then, the basis vectorw₂=[0.3162, 0.3162, 0.3162, 0.3162, 0.3162, −0.3162, −0.3162, −0.3162,−0.3162, −0.3162, 0.0000]′ is stored in the basis vector data file 300,and output to the feature extracting means 230 and the term-documentmatrix updating means 210.

First Repetition in Basis Vector Calculating Means 220 (k=1)

From the foregoing expression (8),w ₂(2)=[0.0102, −0.0098, 0.0102, −0.0096, 0.0104, −0.0105, 0.0095,−0.0103, 0.0097, −0.0105, 0.0102]′w ₂(2)−w ₂(1)=10⁻³×[0.2154, 0.2156, 0.2154, 0.3822, 0.3821, −0.4511,−0.4513, −0.2844, −0.2846, −0.4511, 0.1666]′δ ₂(1)=0.0011

Second Repetition in Basis Vector Calculating Means 220 (k=2)

From the foregoing expression (8),w ₂(3)=[0.0105, −0.0095, 0.0105, −0.0092, 0.0108, −0.0110, 0.0090,−0.0106, 0.0094, −0.0110, 0.0103]′w ₂(3)−w ₂(2)=10⁻³×[0.2624, 0.2626, 0.2624, 0.4413, 0.4411, −0.5152,−0.5154, −0.3364, −0.3366, −0.5152, 0.1786]′δ ₂(2)=0.0013

—syncopated—

A Hundred Nineteenth Repetition in Basis Vector Calculating Means 220(k=119)

From the foregoing expression (8),w₂(120)=[0.3162, 0.3162, 0.3162, 0.3162, 0.3162, −0.3162, −0.3162,−0.3162, −0.3162, 0.0000]′w ₂(120)−w ₂(119)=10⁻⁶×[0.3327, 0.3333, 0.3327, −0.1375, −0.1381,0.3332, 0.3326, −0.1377, −0.1383, 0.3332, −0.4712]′δ ₂(119)=9.8141×10⁻⁷

In the _(feature) extracting means 230, the operations shown in theexpressions (11) and (12) are performed for outputting:y ₂=[0.7071, −0.7071, −0.0000]

-   -   and        p₂=2.2361    -   to the feature data file 400 and the normalizing parameter data        file 450.

From the results set forth above, the feature vectors of the documents1, 2 and 3 in FIG. 1 are respectively [0.5000, 0.7071]′, [0.5000,−0.7071], [0.7071, −0.0000]. Comparing these with the features of theLSA of respective documents shown in the explanation of the prior art,the second element of each vector is of opposite sign but has the sameabsolute value. Accordingly, concerning calculation of similarity in theexpression (2), they have the same nature as the features of LSA.

B. Question of FIG. 3

Here, let us use the basis vectors stored in the basis vector data file300 and the normalizing parameters stored in the normalizing parameterdata file 450 during extraction of the features of the documents of FIG.1. Thereby, execution of the basis vector calculating means 220 andcalculation of the normalizing parameter in the feature extracting means230 are omitted. Let X denote the term-document matrix of FIG. 4.

I. First Iteration in Feature Extracting Means 200 (i=1)

In the term-document matrix updating means 210, E(1) expressed asfollows from the foregoing expression (5) is output to the featureextracting means 230. ${E(1)} = \begin{bmatrix}0 \\0 \\1 \\1 \\1 \\1 \\1 \\0 \\1 \\0 \\0\end{bmatrix}$

In the feature extracting means 230, arithmetic operation according tothe foregoing expressions (11) and (12) is performed using the featurevector w₁ and the normalizing parameter p₁ obtained upon extraction ofthe features of the documents of FIG. 1 to output

 y ₁=[0.6542]

-   -   to the feature data file 400.

II. Second Iteration in Feature Extraction Control Means 200 (i=2)

In the term-document matrix updating means 210, using the feature vectorw₁ obtained upon performing feature extraction of the documents shown inFIG. 1, from the foregoing equation (5), E(2) expressed as follows isoutput to the feature extracting means 230. ${E(2)} = \begin{bmatrix}{- 0.3271} \\{- 0.3271} \\0.6729 \\0.2103 \\0.2103 \\0.6729 \\0.6729 \\{- 0.7897} \\0.2103 \\{- 0.3271} \\{- 0.4626}\end{bmatrix}$

In the feature extracting means 230, arithmetic operation according tothe foregoing expressions (11) and (12) is performed using the featurevector w₂ and the normalizing parameter p₂ obtained upon extraction ofthe features of the documents of FIG. 1 to outputy ₂=[−0.0000]

-   -   to the feature data file 400.

From the result set forth above, the feature vector of the question ofFIG. 3 becomes [0.6542, −0.0000]′, comparing the value explained in theprior art, the second element has the same absolute value.

The present invention has been described in detail with respect topreferred embodiments. It will now be apparent from the foregoing tothose skilled in the art that broader aspect. It is the intention,therefore, in the apparent claims to cover all such changes andmodifications as fall within the true spirit of the invention.

1. A text mining method for extracting features of documents using aterm-document matrix consisting of vectors corresponding to index termsrepresenting the contents of the documents, wherein contributions of theindex terms act on respective elements of the term-document matrix, saidmethod comprising: a basis vector calculating step of calculating abasis vector spanning a feature space, in which mutually associateddocuments and terms are located in proximity with each other, based on asteepest descent method minimizing a cost; a feature extracting step ofcalculating a parameter for normalizing the features using theterm-document matrix and the basis vector and extracting the features onthe basis of the parameter; and a term-document matrix updating step ofupdating the term-document matrix to a difference between theterm-document matrix, to which the basis vector is not applied, and theterm-document matrix, to which the basis vector is applied.
 2. A textmining method for extracting features of documents as claimed in claim1, wherein the cost is defined as a second-order cost of the differencebetween the term-document matrix, to which the basis vector is notapplied, and the term-document matrix, to which the basis vector isapplied.
 3. A text mining method for extracting features of documents asclaimed in claim 2, wherein said basis vector calculating stepcomprises: an initializing step of initializing a value of the basisvector; a basis vector updating step of updating the value of the basisvector; a variation degree calculating step of calculating a variationdegree of the value of the basis vector; a judging step of making ajudgment whether a repetition process is to be terminated or not usingthe variation degree of the basis vector; and a counting step ofcounting the number of times of said repetition process.
 4. A textmining method for extracting features of documents as claimed in claim3, wherein said basis vector updating step updates the basis vectorusing a current value of the basis vector, the term-document matrix andan updating ratio controlling the updating degree of the basis vector.5. A text mining method for extracting features of documents as claimedin claim 4, wherein, when all basis vectors and normalizing parametersrequired in extracting the features have been already obtained, thecalculation of normalizing parameters in said basis vector calculatingstep and the execution of said feature extracting step are omitted, andsaid feature extracting step extracts the features using the basisvectors and the normalizing parameters that have been already obtained.6. A text mining method for extracting features of documents as claimedin claim 3, wherein, when all basis vectors and normalizing parametersrequired in extracting the features have been already obtained, thecalculation of normalizing parameters in said basis vector calculatingstep and the execution of said feature extracting step are omitted, andsaid feature extracting step extracts the features using the basisvectors and the normalizing parameters that have been already obtained.7. A text mining method for extracting features of documents as claimedin claim 2, wherein, when all basis vectors and normalizing parametersrequired in extracting the features have been already obtained, thecalculation of normalizing parameters in said basis vector calculatingstep and the execution of said feature extracting step are omitted, andsaid feature extracting step extracts the features using the basisvectors and the normalizing parameters that have been already obtained.8. A text mining method for extracting features of documents as claimedin claim 1, wherein said basis vector calculating step comprises: aninitializing step of initializing a value of the basis vector; a basisvector updating step of updating the value of the basis vector; avariation degree calculating step of calculating a variation degree ofthe value of the basis vector; a judging step of making a judgmentwhether a repetition process is to be terminated or not using thevariation degree of the basis vector; and a counting step of countingthe number of times of said repetition process.
 9. A text mining methodfor extracting features of documents as claimed in claim 8, wherein saidbasis vector updating step updates the basis vector using a currentvalue of the basis vector, the term-document matrix and an updatingratio controlling the updating degree of the basis vector.
 10. A textmining method for extracting features of documents as claimed in claim9, wherein, when all basis vectors and normalizing parameters requiredin extracting the features have been already obtained, the calculationof normalizing parameters in said basis vector calculating step and theexecution of said feature extracting step are omitted, and said featureextracting step extracts the features using the basis vectors and thenormalizing parameters that have been already obtained.
 11. A textmining method for extracting features of documents as claimed in claim8, wherein, when all basis vectors and normalizing parameters requiredin extracting the features have been already obtained, the calculationof normalizing parameters in said basis vector calculating step and theexecution of said feature extracting step are omitted, and said featureextracting step extracts the features using the basis vectors and thenormalizing parameters that have been already obtained.
 12. A textmining method for extracting features of documents as claimed in claim1, wherein, when all basis vectors and normalizing parameters requiredin extracting the features have been already obtained, the calculationof normalizing parameters in said basis vector calculating step and theexecution of said feature extracting step are omitted, and said featureextracting step extracts the features using the basis vectors and thenormalizing parameters that have been already obtained.
 13. A textmining apparatus for extracting features of documents using aterm-document matrix consisting of vectors corresponding to index termsrepresenting the contents of the document, wherein contributions of theindex terms act on respective elements of the term-document matrix, saidapparatus comprising: basis vector calculating means for calculating abasis vector spanning a feature space, in which mutually associateddocuments and terms are located in proximity with each other, based on asteepest descent method minimizing a cost; feature extracting means forcalculating a parameter for normalizing the features using theterm-document matrix and the basis vector and extracting the features onthe basis of the parameter; and term-document matrix updating means forupdating the term-document matrix to a difference between theterm-document matrix, to which the basis vector is not applied, and theterm-document matrix, to which the basis vector is applied.
 14. A textmining apparatus for extracting features of documents as claimed inclaim 13, wherein the cost is defined as a second-order cost of thedifference between the term-document matrix, to which the basis vectoris not applied, and the term-document matrix, to which the basis vectoris applied.
 15. A text mining apparatus for extracting features ofdocuments as claimed in claim 14, wherein said basis vector calculatingmeans comprises: initializing means for initializing a value of thebasis vector; basis vector updating means for updating the value of thebasis vector; variation degree calculating means for calculating avariation degree of the value of the basis vector; judging means formaking a judgment whether a repetition process is to be terminated ornot using the variation degree of the basis vector; and counting meansfor counting the number of times of said repetition process.
 16. A textmining apparatus for extracting features of documents as claimed inclaim 15, wherein said basis vector updating means updates the basisvector using a current value of the basis vector, the term-documentmatrix and an updating ratio controlling the updating degree of thebasis vector.
 17. A text mining apparatus for extracting features ofdocuments as claimed in claim 16, wherein, when all the basis vectorsand normalizing parameters required in extracting the feature have beenalready obtained, the calculation of normalizing parameters by saidbasis vector calculating means and the execution of said featureextracting means are omitted, and said feature extracting means extractsthe features using the basis vectors and the normalizing parameters thathave been already obtained.
 18. A text mining apparatus for extractingfeatures of documents as claimed in claim 15, wherein, when all basisvectors and normalizing parameters required in extracting the featureshave been already obtained, the calculation of normalizing parameters bysaid basis vector calculating means and the execution of said featureextracting means are omitted, and said feature extracting means extractsthe features using the basis vectors and the normalizing parameters thathave been already obtained.
 19. A text mining apparatus for extractingfeatures of documents as claimed in claim 14, wherein, when all basisvectors and normalizing parameters required in extracting the featureshave been already obtained, the calculation of normalizing parameters bysaid basis vector calculating means and the execution of said featureextracting means are omitted, and said feature extracting means extractsthe features using the basis vectors and the normalizing parameters thathave been already obtained.
 20. A text mining apparatus for extractingfeatures of documents as claimed in claim 13, wherein said basis vectorcalculating means comprises: initializing means for initializing a valueof the basis vector; basis vector updating means for updating the valueof the basis vector; variation degree calculating means for calculatinga variation degree of the value of the basis vector; judging means formaking a judgment whether a repetition process is to be terminated ornot using the variation degree of the basis vector; and counting meansfor counting the number of times of said repetition process.
 21. A textmining apparatus for extracting features of documents as claimed inclaim 20, wherein said basis vector updating means updates the basisvector using a current value of the basis vector, the term-documentmatrix and an updating ratio controlling the updating degree of thebasis vector.
 22. A text mining apparatus for extracting features ofdocuments in text mining as claimed in claim 21, wherein, when all basisvectors and normalizing parameters required in extracting the featurehave been already obtained, the calculation of normalizing parameters bysaid basis vector calculating means and the execution of said featureextracting means are omitted, and said feature extracting means extractsthe features using the basis vectors and the normalizing parameters thathave been already obtained.
 23. A text mining apparatus for extractingfeatures of documents as claimed in claim 20, wherein, when all basisvectors and normalizing parameters required in extracting the featureshave been already obtained, the calculation of normalizing parameters bysaid basis vector calculating means and the execution of said featureextracting means are omitted, and said feature extracting means extractsthe features using the basis vectors and the normalizing parameters thathave been already obtained.
 24. A text mining apparatus for extractingfeatures of documents as claimed in claim 13, wherein, when all basisvectors and normalizing parameters required in extracting the featureshave been already obtained, the calculation of normalizing parameters bysaid basis vector calculating means and the execution of said featureextracting means are omitted, and said feature extracting means extractsthe features using the basis vectors and the normalizing parameters thathave been already obtained.
 25. A computer program product comprising acomputer readable medium, on which a computer program is stored, thecomputer program causing a computer to execute the text mining method asclaimed in claim 1.